Quantative Trading Framework: USD/CAD Exchange Rate & Crude Price
Luke Talman
2023-12-06
Strategy
- Momentum-based trading strategy intended to capitalize on the correlation that exists between crude prices and the CAD/USD exchange rate over certain periods
- Utilizes a weighted 2 and 6 day Rate of Change of USO to predict USD/CAD exchange-rate directional in the near term
- Market Entry/Exit strategy utilizing a 30 day rolling regression on USO and USD/CAD log returns
Rationale
The foundations of the trade rely on principles of currency supply and demand, and their relationship between imports and exports between different economies
- Oil and gas extraction exports represent a significant percentage of Canada’s total exports (19.2% in 2022), with a majority of product going to the US
- Canadian energy products are generally exported to the US in exchange for USD, while Canadian producers incur significant costs in CAD
- When Oil Prices are high, the USD supply increases relative to CAD within Canada, increasing the CADs relative value
- Further, strong energy prices are often accompanied by net economic growth within Canada as producers are able to increase workforce and investment
- Following the above market fundamentals:
- Positive momentum in crude prices should be reflected in a relative increase in CAD, vice versa
- Due to the nature the currency inflow/outflow resulting from Oil Sales and Production (transport times, fixed-price production contracts, futures market etc.), one could expect that the currency-price effect of crude price change may not be fully realized in a single trading day
- The trading strategy within this doc seeks to explore the possibility of this price disconnect
Research
At its foundation, this strategy relies on a positive correlation between energy prices, and the Canadian dollars relative strength
- As highlighted below, this relationship is not always present
Given Canada’s relative economic diversification to some other ‘petrocurrency’ countries, factors such as policy interest rates reinvestment rate in the oil industry, and general macroeconomic trends can reduce this correlation
Recent applicable factors include:
Reduction in investment confidence for Canadian oil with production cap risk, midstream issues, and high marginal costs per barrel
Relative high US policy interest rates
Crude prices changes have less explanatory power in USD/CAD prices changes from 2016 onward
- This reduction in Adj. R Squared will likely reduce trade model efficacy
Model Implementation
Data
Series Selected:
United States Oil Fund (USO)
Exchanged traded security intended to capture the change in USO’s net asset value
- The funds assets are composed of crude oil futures contracts and other oil-related contracts
Under performance relative to WTI spot price in recent time, in part due to negative roll yield associated with period of contango in the oil market
CAD/USD Exchange Rate
Signals & Trades
Signals Utilized
2 & 6 Day Rate of Change (ROC) of daily USO close price
The ROC used to generate a final signal is a weighted average of the two measures, with Alpha and Beta determining the 2 & 6 day weight, respectively
A weighted ROC of > 0 signals a short position in USD/CAD
A weighted ROC of < 0 signals a long position in USD/CAD
Combining ROCs with two different windows intents to reduce noise, given the volatility of daily returns
30 Day Rolling Regression of CAD/USD daily returns on USO daily returns
A statically significant (at Alpha = .1) and slope coefficient < 0 generates positive signal to enter/stay in the market
Any other combinations of signals signals a no trade/exit from the market
The use of these two measures intends capture the direction and significance of the impact USO returns have on CAD/USD returns on a daily basis a given point in time, using recent historical data
Combined Signal
A final signal is generated that combines the long/short directional of the ROC signal, as well as the no trade/exit signal generate via the regression
If the regression signal does not indicated a no trade/exit, a long/short signal is generated in based on the sign of the ROC signal
If the regression signal indicates a no trade/exit, a signal of zero is generated
Corresponding USO price data is not available for all USD/CAD trading days; on days with missing price data, a 0 (no trade/exit) signal is generated
Trades
Trades are generated using the combined signal from the previous day
0 indicated that no market position should be held; no trade will occur, unless it is to close an existing position
1 indicates short position USD/CAD
-1 indicates a long position USD/CAD
Training Period
A training window from 2007-01-01 to 2018-12-31 was selected
Price data for USO begins in 2006-05-01
Maximizes trade data sample size
Includes periods of significant energy price volatility
Optimization
Four parameters are optimized within the model:
Number of days to consider when calculating either un-weighted ROC
Values between 2 and 4 days for the shorter term ROC
Values were considered between 5 and 10 days for the longer term ROC
Alpha and Beta values utilized combining either ROC into a single value
- Values between .2 and .8 at .2 increments were considered, with any final combination summing to 1
The inclusion of a rolling regression significantly increased optimization time complexity; ideally, a broader range of Alpha and Beta’s would be considered
Risk Appetite
- Minimize max drawdown length
- Given the varying correlation between USO and CAD/USD returns, having a strategy that has shorted anticipated drawdowns (per the training set) could provide signs earlier, should the strategy start failing
- Filter for lower 10% percentile
- Given the varying correlation between USO and CAD/USD returns, having a strategy that has shorted anticipated drawdowns (per the training set) could provide signs earlier, should the strategy start failing
- Upper 10% percentileof Omegas
- Ensure reasonable risk-to-reward
- Select highest cumulative return of the subset
Performance
| alpha | beta | roc1 | roc2 | CumReturn |
|---|---|---|---|---|
| 0.3333333 | 0.6666667 | 2 | 6 | 0.4862476 |
| 0.2500000 | 0.7500000 | 2 | 6 | 0.4668141 |
| 0.2000000 | 0.8000000 | 2 | 6 | 0.4621479 |
Optimization results suggest that during the training set, smaller values for both the short and longer term ROC are favorable
Returns and risk both seem to benefit from non-extreme alpha and beta values, suggesting the inclusion of two Rates of Change is of benefit to the model
Fluctuation in variance of annual returns are relatively small, with returns driving larger risk-to-return differences
Extended out-of-market periods frequently occured based upon the rolling correlation signal
- These extended periods of disconnects are suprising, particularly before 2016 when the static relationship weaken
Periods of increases currency exchange rate volatility are typically accompanied by increases the USO ROC measure
- Unsurprising, given the economic foundations of the relationship
Cumulative returns are poor considering risk; possible explanations include:
In its current form, the model fails to fully capture the price-impact crude prices have on the USD/CAD exchange rate
Crude prices are effectively priced in to USD/CAD rates on an intra-day horizon
Testing Period
Returns were strong though COVID volatility
Aligns patterns obeserved within the training set
Suggests that some variation of this trading model may provide utlity in higher volatility periods
In-market percent was relatively unchanged from train period
Does not align with general sentiment, and statistical measures suggesting the CAD has somewaht disconnected from crude prices in recent time
- Possibly indicative of a model failure
Limitations & Learnings
Limitations
As it stands, the model fails to consider transaction costs
A historical-looking non-lagged rolling regression is an imperfect measure to capture whether or not pricing dynamics that the model seeks to capitalize on are occurring
Simply lagging USO returns a day does not solve the issue, given the 2 and 6 day ROC window
Further statistical testing/modelling is required to find more optimal metric to generate a no trade/exit signal
Ideally position size would also be a function of the strength of the relationship between USO and USD/CAD prices at any given time
Learnings
- Data frequency and timeliness significantly limit the development of trading models that rely on non-financial market data sources
- Established and understood causal relationships are, unsurprisingly, challenging to profit from
- Attempting to leverage statistical measures in an applied setting where ones understanding is tested through a metric such as P&L is good at highlighting knowledge gaps